Super resolution is one of the methods to restore degraded images. Exemplar based super-resolution reconstructs higher resolution images from input low resolution images by using pre-specified high-resolution-and-low-resolution image pairs in training set.
An example of exemplar-based super-resolution method is disclosed in PTL 1. As shown in FIG. 1 of PTL 1, the method disclosed in PTL 1 mainly consists of training stage and inferring stage.
As illustrated in FIG. 1 of PTL 1, in the training phase, scenes (high resolution; HR) and images (low resolution) are acquired by synthesis or measurement. The acquired high resolution and low resolution images are then partitioned into overlapping pieces called “patches” as shown in FIG. 2 of PTL 1. Each low-resolution patch is linked to the corresponding high resolution patch as pairs in the training stage.
In the inferring phase, an unknown image is also partitioned into patches. For each patch of the unknown image, the training data is searched to find a collection of candidates which best explains the unknown patch. Selection of patch is determined by choosing the training patch which has the highest score (e.g. smallest L2 norm i.e. the nearest neighbour) among the collection of candidate patches. The inferred scene is then reconstructed by combining these chosen training patches.
Another example of exemplar-based super resolution method is disclosed in PTL 2. PTL 2 also discloses an image processing device which also includes training phase and inferring phase. The training phase also includes a dictionary storing data and the associated blurred patches. The inferring phase in PTL 2 calculates a weighed degree-of-similarity between input patches and blurred patches in the dictionary. The weight is calculated by using degree of doubt (K) which depends on similarity of the selected HR to candidate HR patches in the dictionary. The inferred scene is reconstructed by combining patches which have lowest degree of doubt (K).
PTL 3 discloses an object recognition method which mainly includes three phases: choosing the most similar object from database, score calculation and sorting of similar objects from database. The score calculation includes similarity score which calculates the similarity between query feature vector and database feature vectors. ID (Identifier) numbers are assigned to objects in database so that each type of objects has one ID. Similarity score is calculated by sum of scores between query and database objects of the same ID.